LLM Wiki

A personal knowledge base that builds and maintains itself.

Drop articles, notes, PDFs, or Reddit threads into a folder. Your AI reads everything, builds a structured wiki with cross-references, detects contradictions between sources, and keeps it all up to date — automatically.

You just read and ask questions.

Inspired by Andrej Karpathy's LLM Wiki pattern. Extended with auto-discovery, Reddit scanning, GitHub tracking, and ready-made variants for common use cases.

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Why not just use RAG?

	Traditional RAG	LLM Wiki
Knowledge	Re-retrieved fresh every query	Compiled once, maintained continuously
Cross-references	None	Built and maintained automatically
Contradictions	Not detected	Flagged immediately on ingest
Compounding	Flat	Grows richer over time
Infrastructure	Vector DB + embeddings	Just folders + markdown

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Quick Start

Option A — Start with a ready-made variant (recommended)

Don't configure from scratch. Pick a variant that matches your use case — config, schema, and demo data are pre-tuned:

git clone https://github.com/mduongvandinh/llm-wiki.git my-wiki
cd my-wiki

# Install skill (Claude Code only — other tools skip this)
cp -r skills/llm-wiki ~/.claude/skills/llm-wiki

# Pick your variant:
/llm-wiki setup book-companion      # 📚 Reading companion
/llm-wiki setup competitive-intel   # 🎯 Track competitors
/llm-wiki setup job-search          # 💼 Job search research

Option B — Start from scratch

git clone https://github.com/mduongvandinh/llm-wiki.git my-wiki
cd my-wiki

cp config.example.yaml config.yaml
# Edit config.yaml: set your topics, keywords, feeds

Then drop content into raw/articles/ and run your first cycle.

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Variants

Pre-configured templates for specific use cases. One command setup, includes demo data.

	Variant	Use Case	Key Command
📚	book-companion	Build a companion wiki as you read — characters, timeline, factions, lore auto-extracted	book-summary · demo
🎯	competitive-intel	Track competitors, detect pricing/feature changes, generate battlecards	competitive-brief "Linear"
💼	job-search	Research target companies, prep interview 1-pagers	interview-prep "Stripe"

→ See all variants →

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Using with Your AI Tool

LLM Wiki works with any AI coding assistant. The wiki is just folders and markdown — the AI reads CLAUDE.md (or AGENTS.md) to understand the schema and rules, then operates on files.

Claude Code

The primary supported tool. Has a slash command skill for the best experience.

Install the skill:

cp -r skills/llm-wiki ~/.claude/skills/llm-wiki

Commands:

/llm-wiki setup book-companion     # Set up a variant
/llm-wiki run                       # Full cycle: discover → ingest → lint
/llm-wiki ingest                    # Process new files in raw/
/llm-wiki query "What is X?"        # Ask questions against the wiki
/llm-wiki discover                  # Auto-find new sources from the web
/llm-wiki lint                      # Health check: contradictions, orphans, gaps
/llm-wiki status                    # Overview of wiki state
/llm-wiki digest                    # Daily brief: new sources, insights, changes
/llm-wiki book-summary              # Structured book summary (book-companion variant)
/llm-wiki competitive-brief "Name"  # Battlecard for a competitor
/llm-wiki interview-prep "Company"  # Interview prep 1-pager

Auto-run:

/loop 1h /llm-wiki run    # Runs every hour in current session
/loop 2h /llm-wiki run    # Every 2 hours

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Cursor

Cursor reads AGENTS.md from the project root automatically. No installation needed.

Open the project in Cursor, then chat:

Follow AGENTS.md. Run full cycle: discover new sources, ingest into wiki, then lint.

Follow AGENTS.md. Ingest everything new in raw/ and update the wiki.

Follow AGENTS.md. Query the wiki: what do we know about [topic]?

Follow AGENTS.md. Run competitive-brief for "Linear" and save to outputs/.

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GitHub Copilot (VS Code)

Copilot reads .github/copilot-instructions.md automatically when you open the project. No installation needed.

Open project in VS Code, then use Copilot Chat:

Read CLAUDE.md then run discover, ingest, and lint for this wiki.

Based on the wiki schema in CLAUDE.md, ingest the new files in raw/articles/.

Query the wiki: summarize everything we know about [topic].

Note: Copilot Chat doesn't support background loops. Run manually or use system scheduler.

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Windsurf (Codeium)

Windsurf reads AGENTS.md or .windsurfrules from the project root.

Chat in Windsurf:

Read AGENTS.md. Run the full llm-wiki cycle: discover → ingest → lint.

Read AGENTS.md. I've added new files to raw/articles/. Please ingest them into the wiki.

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Gemini CLI

Gemini CLI reads GEMINI.md from the project root (or falls back to AGENTS.md).

Create GEMINI.md (copy from AGENTS.md):

cp AGENTS.md GEMINI.md

Then run:

gemini "Read GEMINI.md and run full cycle: discover, ingest, lint"
gemini "Read GEMINI.md and query the wiki: what do we know about [topic]?"

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Codex CLI (OpenAI)

Codex reads AGENTS.md from the project root.

codex "Read AGENTS.md and run full cycle: discover → ingest → lint"
codex "Read AGENTS.md and ingest new files in raw/"
codex "Read AGENTS.md and query: summarize the wiki on [topic]"

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Aider

Aider works best with explicit file references.

aider --message "Read CLAUDE.md to understand the schema. Run ingest on all new files in raw/articles/. Update wiki/ accordingly." CLAUDE.md raw/articles/*.md

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Continue.dev

Add CLAUDE.md to Continue's context, then chat:

@CLAUDE.md Ingest the new articles in raw/ and update the wiki with entities, concepts, and cross-references.

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Tool Comparison

Tool	Config File	Skill/Slash Command	Auto-loop	Best For
Claude Code	CLAUDE.md	✅ /llm-wiki	✅ /loop	Full experience, all features
Cursor	AGENTS.md	❌ chat only	❌	IDE-integrated workflow
GitHub Copilot	.github/copilot-instructions.md	❌ chat only	❌	VS Code users
Windsurf	AGENTS.md	❌ chat only	❌	VS Code alternative
Gemini CLI	GEMINI.md	❌ chat only	via cron	Google ecosystem
Codex CLI	AGENTS.md	❌ chat only	via cron	OpenAI preference
Aider	CLAUDE.md	❌ explicit	via cron	Terminal-only

All tools use the same raw/, wiki/, outputs/ folder structure. The AI config file tells each tool how to operate — same content, different filenames.

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Auto-Run Without Claude Code

For tools without /loop, use your system scheduler.

Windows — Task Scheduler (run once as Admin):

powershell -ExecutionPolicy Bypass -File "scripts/setup-scheduler.ps1"

macOS / Linux — crontab:

# Claude Code (headless)
0 */2 * * * cd /path/to/my-wiki && claude --print "/llm-wiki run" >> outputs/auto-run.log 2>&1

# Codex
0 */2 * * * cd /path/to/my-wiki && codex "Read AGENTS.md and run full cycle" >> outputs/auto-run.log 2>&1

# Gemini CLI
0 */2 * * * cd /path/to/my-wiki && gemini "Read GEMINI.md and run full cycle" >> outputs/auto-run.log 2>&1

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How It Works

┌─────────────────────────────────────────────────┐
│              YOU  (read & ask)                   │
├──────────────┬──────────────┬────────────────────┤
│   wiki/      │   outputs/   │   wiki-viewer.html │
│  (Obsidian)  │  (reports)   │   (browser view)   │
├──────────────┴──────────────┴────────────────────┤
│              LLM  (writes & maintains)            │
├──────────┬──────────┬──────────┬─────────────────┤
│ discover │  ingest  │  query   │      lint        │
│ (find)   │ (process)│ (answer) │  (health check)  │
├──────────┴──────────┴──────────┴─────────────────┤
│   raw/        │  config.yaml   │   CLAUDE.md      │
│  (sources)    │  (settings)    │   (schema/rules) │
└───────────────┴────────────────┴──────────────────┘

Main flow: discover → raw/ → ingest → wiki/ → query → outputs/

Self-improvement loop: lint finds gaps → discover finds sources → ingest fills them → wiki grows richer

Core Workflows

discover — Searches the web for new sources based on your topics in config.yaml. Can scan Reddit for pain points, track GitHub trending repos, follow RSS feeds, and snowball from references found in existing sources.

ingest — Reads each new file in raw/. Extracts entities, concepts, and key claims. Creates wiki pages. Adds cross-references. Flags contradictions with existing content. One source typically creates 5–15 wiki pages.

query — Reads wiki/INDEX.md, finds relevant pages, synthesizes an answer with citations. Valuable answers are automatically saved as synthesis pages.

lint — Scans the entire wiki for: contradictions, orphan pages, broken links, stale claims, and knowledge gaps. Updates .discoveries/gaps.json so discover knows what to find next.

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Adding Sources

Drop a file manually

Create a markdown file in raw/articles/ with frontmatter, then run ingest:

---
title: "Article Title"
url: "https://example.com/article"
author: "Author Name"
discovered: 2026-04-14
topic: "your topic"
---

Article content here...

For book notes (book-companion variant), add chapter: N:

---
title: "Dune Chapter 5 Notes"
chapter: 5
book: "Dune"
---

For competitor data (competitive-intel variant), add competitor: "Name":

---
title: "Linear — Pricing Page"
competitor: "Linear"
---

Obsidian Web Clipper

1. Install Obsidian Web Clipper browser extension
2. See an interesting article → click clip → save to raw/articles/
3. Run ingest

Auto-discovery

Configure config.yaml with topics, Reddit subreddits, GitHub orgs, and RSS feeds — the discover command handles the rest.

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Viewing Your Wiki

Obsidian (recommended): File → Open vault → select your wiki folder → Graph View shows connections

Browser: Open wiki-viewer.html — mobile-first dark theme dashboard with graph view

Any markdown editor: All wiki pages are plain .md files

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Folder Structure

my-wiki/
├── CLAUDE.md                    ← Schema + rules for Claude Code
├── AGENTS.md                    ← Schema + rules for Cursor, Codex, Windsurf, Aider
├── GEMINI.md                    ← Schema + rules for Gemini CLI (copy of AGENTS.md)
├── .github/
│   └── copilot-instructions.md  ← Schema for GitHub Copilot
├── config.yaml                  ← Your topics, feeds, schedule
├── config.example.yaml          ← Template to start from
├── wiki-viewer.html             ← Browser-based wiki viewer
│
├── variants/                    ← Ready-made use case templates
│   ├── book-companion/          ← 📚 Reading companion
│   ├── competitive-intel/       ← 🎯 Competitor tracking
│   └── job-search/              ← 💼 Job search research
│
├── raw/                         ← YOUR SOURCES (LLM never modifies these)
│   ├── articles/                ← Web articles, blog posts, papers
│   ├── reddit/                  ← Reddit threads, pain points
│   ├── notes/                   ← Personal notes
│   └── media/                   ← Screenshots, diagrams
│
├── wiki/                        ← LLM-maintained wiki (don't edit manually)
│   ├── INDEX.md                 ← Master catalog of all wiki pages
│   ← LOG.md                     ← Activity log
│   ├── entities/                ← People, orgs, tools, projects
│   ├── concepts/                ← Ideas, patterns, methodologies
│   ├── sources/                 ← Summary of each raw source
│   └── syntheses/               ← Analysis, comparisons, rankings
│
├── outputs/                     ← Query results, reports, battlecards, briefs
├── .discoveries/                ← Auto-discovery metadata
│   ├── feeds.json               ← Sources being tracked
│   ├── gaps.json                ← Knowledge gaps to fill
│   └── history.json             ← Processed sources (dedup)
│
└── skills/llm-wiki/SKILL.md     ← Claude Code skill definition

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Configuration

All behavior is controlled by config.yaml. Copy config.example.yaml to get started.

Key sections:

wiki:
  name: "My Wiki"
  language: "en"   # Any language — wiki content will be in this language

topics:
  - name: "Topic Name"
    keywords: ["keyword1", "keyword2"]
    priority: high

feeds:
  reddit:
    enabled: true
    subreddits_pain_points:
      - sub: "r/SaaS"
  hackernews:
    enabled: true
    min_score: 50
  github_trending:
    enabled: true
    languages: ["python", "typescript"]

schedule:
  run:
    loop_interval: "1h"   # For /loop in Claude Code

→ See config.example.yaml for the full reference with all options.

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Commands Reference

Command	Description
setup <variant>	Initialize wiki from a variant template (book-companion, competitive-intel, job-search)
init "Topic"	Add a new topic to an existing wiki
ingest	Process all new files in raw/
query "..."	Answer a question using the wiki; saves valuable answers
discover	Auto-find new sources from the web based on topics
run	Full cycle: discover → ingest → lint
lint	Health check: contradictions, orphans, gaps, stale content
status	Show wiki stats and health overview
digest	Daily brief: new sources, top insights, detected changes
pain-rank	Rank pain points by business opportunity (Reddit data)
book-summary	Structured book summary: cast, timeline, themes, mysteries
competitive-brief "Name"	Battlecard 1-pager for a specific competitor
interview-prep "Company"	Interview prep 1-pager for a target company

For tools without slash commands, use the equivalent natural language prompt:

Read CLAUDE.md (or AGENTS.md). Then [describe what you want].

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FAQ

What AI model do I need? Any capable model with tool use (web search, file read/write). Claude Sonnet/Opus recommended. Works with GPT-4o, Gemini 1.5 Pro, and others via their respective CLI tools.

How much does it cost to run? A typical /llm-wiki run cycle (discover + ingest 5 sources + lint) costs $0.10–0.40 depending on wiki size and source length. Running every 2 hours = ~$50/month.

My wiki is getting large. How do I query it efficiently? The skill reads wiki/INDEX.md first to find relevant pages, then reads only those. Even large wikis (500+ pages) query efficiently because you never load everything at once.

Can I use this for a team? Yes — put the wiki folder in a shared Git repo. Each team member runs ingest on their own sources; the wiki is a shared output. Conflicts are resolved by lint.

Does it hallucinate? The query and ingest commands only use information from your raw/ sources. They cannot add claims not supported by your sources. If something is uncertain, the wiki page will say so.

Can I write in a language other than English? Yes. Set wiki.language in config.yaml. The LLM will write all wiki content in that language while keeping file names and frontmatter in English.

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Credits

• Original pattern: Andrej Karpathy — LLM Wiki
• Inspiration: Nick Spisak — How to Build Your Second Brain
• Built for: Claude Code (Anthropic) + compatible with all major AI coding assistants
• Wiki viewer: Obsidian

License

MIT — use it, fork it, build variants, share back.